In smart item environments data associated with product usage (e.g., product lifecycle) and environmental data (e.g., humidity) may be captured via a multitude of sensors (e.g., pressure, temperature, mileage). This data may be exploited to guide and optimize production automation processes as well as complex business decisions. Some applications may directly consume streaming data, wherein the knowledge regarding current data and data quality (DQ) may be critical. Sensor data may further need to be stored in a database for further processing. A potential problem associated with sensor data is restricted data quality. Limited resolution and precision are examples of sensor inherent, physical restrictions. Further, sensor data quality may be decreased by sensor failures and malfunctions due to real world application environments such as an industrial shopfloor or mobile devices. Resolving data quality restrictions resulting directly from system components and environment may result in a significant cost increase for better sensors (e.g., with higher precision) or sensor shielding.
Measured sensor data may be used in production automation processes that are based on measured sensor stream data for many applications. For example, pressure sensors may be used in antilock braking systems (ABS), molding machines, compactors or hydraulic load-sensing systems, wherein a fine sensor accuracy may be important for the control cycle during wide sensor range. As another example, stream data may be monitored by a human to detect irregularities, for example, for immediate maintenance. However, if the streaming sensor data is incorrect or misleading, sensor data may lead to faulty deduced decisions, and thus, data quality restrictions in sensor data streams may benefit from a careful resolution. Thus, it may be desirable to provide techniques which may include data quality in data streams.
Further, management of large amounts of measurement data and data quality may result in significant overhead in storage and computing resources. Thus, it may be desirable to provide techniques for management of data quality in data streams and in relational metadata models.